Fusion of non-visual and visual sensors for human tracking

Human tracking is an extensively researched yet still challenging area in the Computer Vision field, with a wide range of applications such as surveillance and healthcare. People may not be successfully tracked with merely the visual information in challenging cases such as long-term occlusion. Thus, we propose to combine information from other sensors with the surveillance cameras to persistently localize and track humans, which is becoming more promising with the pervasiveness of mobile devices such as cellphones, smart watches and smart glasses embedded with all kinds of sensors including accelerometers, gyroscopes, magnetometers, GPS, WiFi modules and so on. In this thesis, we firstly investigate the application of Inertial Measurement Unit (IMU) from mobile devices to human activity recognition and human tracking, we then develop novel persistent human tracking and indoor localization algorithms by the fusion of non-visual sensors and visual sensors, which not only overcomes the occlusion challenge in visual tracking, but also alleviates the calibration and drift problems in IMU tracking --Abstract, page iii

Grid multi-category response logistic models.

BackgroundMulti-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations.MethodsThis paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.ResultsSimulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models.ConclusionsThe grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models

Optimal design of blade parameters for fracturing tea-picking machine

The blade is one of the most critical components in the fracturing tea-picking machine, and this study is conducted to optimize the blade's working parameters. In this study, the effects of blade width, blade thickness, and cutting angle on the maximum fracturing force of tea stems were analyzed using the L9 (34) standard orthogonal table, with the maximum fracturing force used as the evaluation index. The results indicate that the main factors affecting the maximum fracturing force (MFF) of tea stems are cutting angle (CA), blade width (BW), and blade thickness (BT) in that order. Furthermore, microscopic observation of the fracture surface revealed that compared with the thickness of the other two blades, the thickness of 0 mm caused the cross-section uneven and had lots of burrs, correspondingly resulting in the section's oxidation and the deterioration of tea leaf quality. Therefore, the optimal combination of design parameters was a cutting angle of 90°, a blade width of 2.0 mm, and a blade thickness of 0.5 mm. The findings of this study can provide reference for blade design to reduce the fracturing force of tea-picking machines, lower the working power consumption, and improve the quality of freshly plucked tea leaves

Nonlinear Flow Characteristics and Horizontal Well Pressure Transient Analysis for Low-Permeability Offshore Reservoirs

Threshold pressure gradient (TPG) and stress sensitivity which cause the nonlinear flow in low permeability reservoirs were carried out by experiments. Firstly, the investigation of existing conditions of TPG for oil flow in irreducible water saturation low-permeability reservoirs was conducted and discussed, using the cores from a real offshore oilfield in China. The existence of TPG was proven. The relationship between TPG and absolute permeability was obtained by laboratory tests. TPG increases with decreasing absolute permeability. Then, stress sensitivity experiment was carried out through depressurizing experiment and step-up pressure experiment. Permeability modulus which characterizes stress sensitivity increases with decreasing absolute permeability. Consequently, a horizontal well pressure transient analysis mathematical model considering threshold pressure gradient and stress sensitivity was established on the basis of mass and momentum conservation equations. The finite element method (FEM) was presented to solve the model. Influencing factors, such as TPG, permeability modulus, skin factor, wellbore storage, horizontal length, horizontal position, and boundary effect on pressure and pressure derivative curves, were also discussed. Results analysis demonstrates that the pressure transient curves are different from Darcy’s model when considering the nonlinear flow characteristics. Both TPG and permeability modulus lead to more energy consumption and the reservoir pressure decreases more than Darcy’s model

Computing the Newton Potential in the Boundary Integral Equation for the Dirichlet Problem of the Poisson Equation

Evaluating the Newton potential is crucial for efficiently solving the boundary integral equation of the Dirichlet boundary value problem of the Poisson equation. In the context of the Fourier-Garlerkin method for solving the boundary integral equation, we propose a fast algorithm for evaluating Fourier coefficients of the Newton potential by using a sparse grid approximation. When the forcing function of the Poisson equation expressed in the polar coordinates has mth-order bounded mixed derivatives, the proposed algorithm achieves an accuracy of order (n-m log3 n), with requiring (n log2 n) number of arithmetics for the computation, where n is the number of quadrature points used in one coordinate direction. With the help of this algorithm, the boundary integral equation derived from the Poisson equation can be efficiently solved by a fast fully discrete Fourier-Garlerkin method

Environment-independent mmWave Fall Detection with Interacting Multiple Model

The ageing society brings attention to daily elderly care through sensing technologies. The future smart home is expected to enable in-home daily monitoring, such as fall detection, for seniors in a non-invasive, non-cooperative, and non-contact manner. The mmWave radar is a promising candidate technology for its privacy-preserving and non-contact manner. However, existing solutions suffer from low accuracy and robustness due to environment dependent features. In this paper, we present FADE (\underline{FA}ll \underline{DE}tection), a practical fall detection radar system with enhanced accuracy and robustness in real-world scenarios. The key enabler underlying FADE is an interacting multiple model (IMM) state estimator that can extract environment-independent features for highly accurate and instantaneous fall detection. Furthermore, we proposed a robust multiple-user tracking system to deal with noises from the environment and other human bodies. We deployed our algorithm on low computing power and low power consumption system-on-chip (SoC) composed of data front end, DSP, and ARM processor, and tested its performance in real-world. The experiment shows that the accuracy of fall detection is up to 95\%

IoT Networking: Path to Ubiquitous Connectivity

University of Minnesota Ph.D. dissertation. August 2019. Major: Computer Science. Advisor: Tian He. 1 computer file (PDF); xii, 105 pages.Internet of Things (IoT) is upon us with the number of IoT connected devices reach- ing 17.68 billion in the year 2016 and keeps an increasing rate of 17%. The popularity of IoT brings the prosperity and diversity of wireless technologies as one of its founda- tions. Existing wireless technologies, such as WiFi, Bluetooth, and LTE, are evolving and new technologies, such as SigFox and LoRa, are proposed to satisfy various needs under emerging application scenarios. For example, WiFi is evolving to provide higher throughput with the novel 802.11ac technology and the Bluetooth SIG has proposed the Bluetooth Low Energy (BLE) technology to support low-power applications. However, wireless technologies are victims of their own success. The vastly increasing wireless devices compete for the limited wireless spectrum and result in the performance degradation of each device. What makes it worse is that diverse wireless devices are using heterogeneous PHY and MAC layers designs which are not compliant with each other. As a result, sophisticated wireless coordination methods working well for each homogeneous technology are not applicable in the heterogeneous wireless scenario for the failure to communicate among heterogeneous devices. This dissertation aims at fundamentally solving the burden of communication in today’s heterogeneous wireless environment. Specifically, we try to build direct communication among heterogeneous wireless technologies, referred to as the cross-technology communication (CTC). It is counter-intuition and long believed impossible, but we find two opportunities in both the packet level and physical (PHY) layer to make the challenging mission possible. First, wireless devices are commonly able to do energy-sensing of wireless packets in the air. Energy sensing is capable to figure out packet-level information, such as the packet duration and timing. Based on the energy-sensing capability, we design DCTC, a CTC technology that piggybacks cross-technology messages within the timing of transmitted wireless packets. Specifically, we slightly perturb the timing of packets emitted from a wireless device to form detectable energy patterns to establish CTC. Testbed evaluation has shown that we can successfully transmit information at 760bps while keeping the delay of each packet no longer than 0.5ms under any traffic pattern. Second, in the PHY layer, high-end wireless technologies are flexible, i.e., a larger symbol set, in the modulation and demodulation. With careful choices of symbols, those wireless technologies are able to emulate and decode the PHY layer signal of a low-end one. We propose two systems BlueBee and XBee which aim at building direct com- munication between two heterogeneous IoT technologies, Bluetooth and ZigBee, with the idea of signal emulation and cross-decoding respectively. The former achieves signal emulation by carefully choosing the Bluetooth payload bits so that the output signal emulates a legitimate ZigBee packet which can be successfully demodulated by a com- modity ZigBee devices without any changes. The latter proposes a general method to support the bidirectional communication in the PHY-layer CTC by moving the complex- ity to the high-end receiver for the demodulation of signal from a low-end transmitter. Our testbed evaluation has shown that our technologies successfully boost the data rate of the state of the arts by over 10,000x times, which is approaching the ZigBee standard. This result makes CTC possible to play more roles in real-time applications, such as network coordination. In summary, this dissertation provides a new communication paradigm in a heteroge- neous wireless environment, which is to provide direct communication for heterogeneous wireless devices. Such communication is built upon two opportunities: (i) wireless de- vices are capable to sense energy in the air so that specifically designed energy patterns can transmit cross-technology information; (ii) a high-end wireless technology is more flexible and possible to emulate and demodulate the signal from a low-end technology for communication. The technologies developed in the dissertation will be the build- ing blocks for the future designs of efficient channel coordination and ubiquitous data exchange among heterogeneous wireless devices

Evolution-Peak based Evolutionary Control and Analysis on Carbon Emission System of the United States

AbstractBased on the status quo of carbon emissions in USA and the international crude oil price fluctuations, this paper introduces control index and critical time of carbon emissions to find a new dynamic evolutionary model of carbon emissions of the States, deducing relative theories, such as Change Trends Theorem and Evolutionary Theorem. The critical time in the economic period is determined based on the evolutionary situation of the international crude oil price peaks, and it can be divided into four time intervals. Least-square method is used to analyze the dynamic evolutionary system of carbon emissions in the four time intervals with data provided by the international energy agency (IEA). Based on the nonlinear dynamic evolutionary model, the paper predicts carbon emissions by means of control index and control function, which facilitates carbon policy regulation and the system's external influence, and creates unique dynamic evolutionary factors of carbon emissions corresponding with the real situation of the United States. The financial crisis and shale gas large-scale mining have significantly changed America's energy supply structure. With the economy running upward, carbon emissions have a tendency to increase again. To achieve the goal of its reduction, different policies should be adopted by the US government. In this essay, the influence of the control index and the effect of critical time of carbon emissions to control function are analyzed. In addition, the dynamic evolutionary model is introduced and evolutionary scenario analysis is also conducted by modulating evolutionary coefficient and critical time

Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation

A log-linear cognitive diagnostic model (LCDM) is estimated via a global optimization approach- differential evolution optimization (DEoptim), which can be used when the traditional expectation maximization (EM) fails. The application of the DEoptim to LCDM estimation is introduced, explicated, and evaluated via a Monte Carlo simulation study in this article. The aim of this study is to fill the gap between the field of psychometric modeling and modern machine learning estimation techniques and provide an alternative solution in the model estimation

Task-driven Semantic-aware Green Cooperative Transmission Strategy for Vehicular Networks

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the intermittently connected vehicular networks, fulfilling the 6G vision of semantic and green communications. Specifically, we introduce a task-driven knowledge graph (KG)-assisted semantic communication system, and model the KG into a weighted directed graph from the viewpoint of transmission. Meanwhile, we identify three predictable parameters about the individual vehicles to perform the following anticipatory analysis. Firstly, to facilitate semantic extraction, we derive the closed-form expression of the achievable throughput within the delay requirement. Then, for the extracted semantic representation, we formulate the mutually coupled problems of semantic unit assignment and predictive relay selection as a combinatorial optimization problem, to jointly optimize the energy efficiency and semantic transmission reliability. To find a favorable solution within limited time, we proposed a low-complexity algorithm based on Markov approximation. The promising performance gains of the PreCMTS are demonstrated by the simulations with realistic vehicle traces generated by the SUMO traffic simulator.Comment: Accepted by IEEE Transactions on Communication